Running a Sample App With the Tracking API

The programs in examples use the MLflow Tracking API. For instance, run::

python examples/quickstart/mlflow_tracking.py

This program will use MLflow Tracking API <https://mlflow.org/docs/latest/tracking.html>_,
which logs tracking data in ./mlruns. This can then be viewed with the Tracking UI.

Launching the Tracking UI

The MLflow Tracking UI will show runs logged in ./mlruns at <http://localhost:5000>_.
Start it with::

mlflow ui

Note: Running mlflow ui from within a clone of MLflow is not recommended - doing so will
run the dev UI from source. We recommend running the UI from a different working directory, using the
--file-store option to specify which log directory to run against. Alternatively, see instructions
for running the dev UI in the contributor guide <CONTRIBUTING.rst>_.

Running a Project from a URI

The mlflow run command lets you run a project packaged with a MLproject file from a local path
or a Git URI::

See examples/sklearn_elasticnet_wine for a sample project with an MLproject file.

Saving and Serving Models

To illustrate managing models, the mlflow.sklearn package can log scikit-learn models as
MLflow artifacts and then load them again for serving. There is an example training application in
examples/sklearn_logisitic_regression/train.py that you can run as follows::